Support FP8 Quantization and Inference Run on Intel Gaudi (HPU) using INC (Intel Neural Compressor)

[nirda7]

This PR adds support to FP8 Quantization and Inference run on Intel Gaudi (HPU) using INC (Intel Neural Compressor).
Currently, quantization is validated only in Llama models.

• Measurement files are required to run quantized models with vLLM on Gaudi accelerators. The FP8 model calibration procedure is described in the vllm-hpu-extention package: https://github.com/HabanaAI/vllm-hpu-extension/tree/main/calibration/README.md
  Measurements are device dependent - Don't use measurements collected on Gaudi3 on Gaudi2 accelerators as it might cause accuracy issues.

Running Inference in FP8 with INC:
Specify quantization method "inc" and kv cache dtype "fp8_inc" as parameters to the the LLM object.
It will require to set an environment variable "QUANT_CONFIG" which will point to a 'JSON config file' (https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Quantization/Inference_Using_FP8.html#supported-json-config-file-options) in QUANTIZE mode. Make sure there are measurement files/scale files in the folder specified as the "dump_stats_path" in the json config file. (If none exists, scale files are generated during the inference run using the measurement files)
At the end of the run, the model executor's shutdown method must be called.

More information on vLLM quantization using INC will be shown here (added in this PR): https://github.com/vllm-project/vllm/blob/main/docs/source/features/quantization/inc.md

This PR adds a new flag "weights_load_device" which allows uploading the model's (unquantized) weights onto a different device than the device on which the model will run. If not provided the behavior is kept by using the device specified in the device config.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @nirda7.

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